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- W2065985276 abstract "In this paper, we address the issue of deciding when to stop active learning for building a labeled training corpus. Firstly, this paper presents a new stopping criterion, classification-change, which considers the potential ability of each unlabeled example on changing decision boundaries. Secondly, a multi-criteria-based combination strategy is proposed to solve the problem of predefining an appropriate threshold for each confidence-based stopping criterion, such as max-confidence, min-error, and overall-uncertainty. Finally, we examine the effectiveness of these stopping criteria on uncertainty sampling and heterogeneous uncertainty sampling for active learning. Experimental results show that these stopping criteria work well on evaluation data sets, and the combination strategies outperform individual criteria." @default.
- W2065985276 created "2016-06-24" @default.
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- W2065985276 date "2008-01-01" @default.
- W2065985276 modified "2023-10-17" @default.
- W2065985276 title "Multi-criteria-based strategy to stop active learning for data annotation" @default.
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- W2065985276 doi "https://doi.org/10.3115/1599081.1599223" @default.
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